I am continuously confronted with the following balancing act - easy to describe - difficult to judge, decide and act upon...

One side looks for new technologies (also called innovation) that can provide us with new scientific insights into the compounds that we would like to develop into new medicines. Here elements like cutting-edge technologies, competitive advantages, novel types of insights, automation, throughput, etc., weigh a lot.

The other side (you could summarize them simply as the more conservative aspects) look at applicability. What kind of data are produced? What do they mean? Do we fully understand how to translate the novel data / information into knowledge and understanding about our chemical structures?

An example to illustrate this are technologies or approaches that will provide us with information about the polypharmacology of compounds - something that we increasingly get with modern high dimensional biology technologies. What do the measurements mean that, e.g., implicate an effect on a molecular level in a context that we cannot directly link to the desired effect? Will they turn out to harm the further development by creating concerns that may not be justified?

Often times such new approaches are being positioned at early stages of the discovery pipeline and are accordingly utilizing simple model systems (e.g., cell lines). Here we will frequently be left with the question of how such findings will translate into the human situation? And furthermore, even if undesired effects were to be correctly indicated, it is usually impossible to predict at what concentration the desired therapeutic effect will be seen in humans. And maybe the detected undesired effects might not yet be induced to an extent that would cause concern when using a concentration that is sufficient for inducing the desired therapeutic effect.

However, while such thoughts are important to consider, it is also necessary to continue to explore new ways of obtaining important information on compounds early to help in prioritizing the structures that have the highest chances of being successful. Tricky...